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• W1961779688 abstract "A support vector classifier was compared to a lexicon-based approach for the task of detecting the stance categories speculation, contrast and conditional in English consumer reviews. Around 3,000 training instances were required to achieve a stable performance of an F-score of 90 for speculation. This outperformed the lexicon-based approach, for which an Fscore of just above 80 was achieved. The machine learning results for the other two categories showed a lower average (an approximate F-score of 60 for contrast and 70 for conditional), as well as a larger variance, and were only slightly better than lexicon matching. Therefore, while machine learning was successful for detecting speculation, a well-curated lexicon might be a more suitable approach for detecting contrast and conditional." @default.
• W1961779688 created "2016-06-24" @default.
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• W1961779688 date "2015-01-01" @default.
• W1961779688 modified "2023-09-24" @default.
• W1961779688 title "Detecting speculations, contrasts and conditionals in consumer reviews" @default.
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• W1961779688 doi "https://doi.org/10.18653/v1/w15-2923" @default.
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